Using machine learning models

Essentia includes algorithms for running inference with data-driven machine learning models that can be used for high-level annotation of music audio. Specifically, Essentia provides a wrapper for TensorFlow that allows using virtually any TensorFlow model within our audio analysis framework.

We provide pre-trained models for various music analysis and classification tasks. Current Essentia models are based on TensorFlow.

Note: We also provide legacy Gaia SVM models based on handcrafted music audio features. These models have been superseded by our current models.


Essentia with TensorFlow support is available for Linux and macOS as a separate Python package, essentia-tensoflow:

pip install essentia-tensorflow

Building Essentia with TensorFlow support

Alternatively, we provide instructions to build Essentia from source and link it against the shared TensorFlow libraries. To avoid collisions when importing both Essentia and TensorFlow in Python, we use the shared libraries within the Python package of TensorFlow itself instead of linking against the official libtensorflow.

Follow these steps to build and install Essentia with TensorFlow support:

At least pip version ≥19.3 is required:

pip3 install --upgrade pip

Install TensorFlow (tested for TensorFlow 2.5, 2.8, 2.12):

pip3 install tensorflow

Clone Essentia:

git clone

Run (may require sudo). This script exposes the shared libraries contained in the TensorFlow wheel so we can link against them:

cd essentia && src/3rdparty/tensorflow/

Install the dependencies for Essentia with Python 3 (may require sudo):

apt-get install build-essential libyaml-dev libfftw3-dev libavcodec-dev libavformat-dev libavutil-dev libavresample-dev python-dev libsamplerate0-dev libtag1-dev libchromaprint-dev python-six python3-dev python3-numpy-dev python3-numpy python3-yaml libeigen3-dev

Configure Essentia with TensorFlow and Python 3:

python3 waf configure --build-static --with-python --with-tensorflow

Build everything:

python3 waf


python3 waf install

Inference with GPU

It is possible to run inference with Essentia Models using GPU when the correct version of the CUDA and CuDNN libraries are installed on your system. We recommend using a package manager such as Conda to install the required components.

These are the recommended steps to follow:

Install Miniconda (or Anaconda). Create and activate a Conda environment:

conda create -n ess python=3.10
conda activate ess

Install CUDA, CuDNN, and essentia-tensorflow:

conda install -c conda-forge -y cudatoolkit=11.2 cudnn=8.1
pip install essentia-tensorflow

It is possible to use CUDA environment variables to control GPU usage. For example, the following line of code launches a script using GPU 1: